Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A computer-implemented method to execute a benefit analysis system for acquiring benefits qualification data, the benefit analysis system being stored at least partially in a memory of a computing system and functioning in conjunction with one or more processors of the computing system, the method being performed by the computing system and comprising: storing, in a shared data store of the benefit analysis system, benefit qualification data corresponding to an individual, the benefit qualification data comprising user input data and data imported from one or more online resources through at least one network; obtaining a profile including the benefit qualification data corresponding to the individual; for each of a plurality of benefit programs corresponding to a plurality of completeness graphs, obtaining at least one completeness graph, each completeness graph representing a completeness graph data structure configured to determine whether the profile satisfies the completeness graph and thereby qualifies for the respective benefit program different from another benefit program, wherein each completeness graph comprises a plurality of interconnecting functional nodes and a plurality of respective arcs, the plurality of interconnecting functional nodes and the plurality of respective arcs representing the rules and regulations for qualifying for the respective benefit program, the plurality of interconnecting functional nodes comprising a beginning node, a termination node, a plurality of intermediate nodes, at least one arc being between the beginning node and at least one intermediate node, at least one arc being between the termination node and at least one intermediate node, and at least one arc being between different intermediate nodes, wherein respective beginning and intermediate nodes correspond to a plurality of questions indicative of respective benefit qualification questions, and respective arcs correspond to respective answers to the respective benefit qualification questions; generating a set of unanswered questions from each of the plurality of the completeness graphs by running the profile against each completeness graph to fill the respective beginning node and intermediate nodes of each completeness graph with data of the profile, each set of unanswered questions being different from another set of unanswered questions from another completeness graph; generating a collection of unanswered questions from a plurality of sets of the unanswered questions from the plurality of the completeness graphs in a manner that eliminates duplicated unanswered questions; and based on an average potential benefit associated with each completeness graph and obtained from a benefit agency database, identifying a high priority unanswered question in the collection of the unanswered questions from the plurality of the completeness graphs by executing a prioritization algorithm.
The invention relates to a computer-implemented benefit analysis system designed to streamline the process of determining an individual's eligibility for various benefit programs. The system addresses the challenge of efficiently gathering and evaluating the necessary qualification data across multiple benefit programs, which often have distinct and complex eligibility criteria. The system stores benefit qualification data for an individual, combining user-provided input with data imported from online resources. This data is used to generate a profile that is then evaluated against multiple benefit programs, each represented by a completeness graph. These graphs are data structures that model the rules and regulations for qualifying for a specific benefit program. Each graph consists of interconnected nodes and arcs, where nodes represent questions related to benefit qualification, and arcs represent possible answers. The graphs include a beginning node, a termination node, and intermediate nodes, with arcs connecting them to form a decision pathway. The system runs the individual's profile against each completeness graph to identify unanswered questions specific to each benefit program. These unanswered questions are then compiled into a collection, with duplicates removed. The system prioritizes the unanswered questions based on the average potential benefit associated with each program, using a prioritization algorithm to identify the most critical questions to address first. This approach ensures that the most impactful eligibility criteria are addressed efficiently, improving the likelihood of successful benefit qualification.
2. The method of claim 1 , further comprising removing a completeness graph not satisfied by the profile from the set of the completeness graphs by running the profile against the completeness graph before forming respective sets of unanswered questions.
A system and method for evaluating completeness of a user profile against a set of predefined completeness graphs. The technology addresses the challenge of assessing whether a user's profile contains sufficient information to meet various completeness criteria, which may be defined by different applications or services. Each completeness graph represents a set of questions or data points that must be answered or provided to satisfy a specific completeness requirement. The method involves comparing the user's profile against each completeness graph to determine which questions or data points remain unanswered. If a completeness graph is not satisfied by the profile, it is removed from the set of completeness graphs before further analysis. This ensures that only relevant and achievable completeness criteria are considered. The system may also generate a set of unanswered questions for each remaining completeness graph, allowing the user to identify missing information. The approach improves efficiency by filtering out unattainable completeness requirements early in the process, reducing unnecessary computations and user interactions. The method is applicable in various domains, including user onboarding, data validation, and compliance checks, where ensuring profile completeness is critical.
3. The method of claim 2 , wherein running the profile against the completeness graph indicates that a likelihood that the profile would satisfy the completeness graph is lower than a threshold value.
A system and method for evaluating data completeness in a database or information system. The technology addresses the challenge of ensuring that data records meet predefined completeness criteria, which is critical for data integrity, analytics, and decision-making. The method involves generating a completeness graph that defines the required attributes or fields for a data profile, such as a user, transaction, or entity record. The completeness graph specifies which attributes are mandatory, optional, or conditional based on other attributes. The system then runs a data profile against this completeness graph to assess whether the profile meets the completeness requirements. If the likelihood that the profile would satisfy the completeness graph is below a threshold value, the system identifies the profile as incomplete. This evaluation may trigger actions such as flagging the profile for review, prompting additional data entry, or applying default values. The method ensures that data records are consistently evaluated against completeness rules, improving data quality and reliability. The system may also adapt the completeness graph dynamically based on evolving data requirements or user feedback.
4. The method of claim 1 , further comprising removing a duplicate unanswered question from the collection of unanswered questions.
This invention relates to systems for managing unanswered questions in a question-and-answer platform. The problem addressed is the inefficiency of repeatedly presenting the same unanswered question to users, which wastes resources and reduces user engagement. The method involves tracking a collection of unanswered questions and identifying duplicate questions within this collection. Once duplicates are detected, they are removed to streamline the question pool. The method may also include analyzing the content of questions to determine similarity, such as by comparing keywords, semantic meaning, or contextual relevance. Additionally, the system may prioritize questions based on factors like recency, relevance, or user activity to improve the likelihood of receiving answers. The removal of duplicate questions ensures that users are presented with a more diverse and efficient set of questions, enhancing the platform's functionality and user experience. The method may be applied in various digital forums, customer support systems, or knowledge-sharing platforms where managing unanswered questions is critical.
5. The method of claim 1 , wherein the prioritization algorithm comprises: accessing a benefit agency database to obtain the average potential benefit corresponding to each completeness graph associated with the respective benefit program; determining a respective impact of each unanswered question in the collection of unanswered questions, the respective impact being equal to a sum of respective average potential benefits of each completeness graph in which the respective unanswered question appears; and identifying an unanswered question in the collection of unanswered questions with a highest impact as the high priority unanswered question.
This invention relates to a method for prioritizing unanswered questions in a benefit program application process, particularly for government or agency benefit programs. The problem addressed is the inefficiency in guiding applicants through complex benefit applications, where incomplete or unanswered questions can delay or prevent approval. The method improves this by dynamically prioritizing which questions should be answered first based on their potential impact on the application's success. The method involves a prioritization algorithm that accesses a benefit agency database to retrieve the average potential benefit associated with each completeness graph linked to a benefit program. A completeness graph represents the state of an application based on answered and unanswered questions. The algorithm then calculates the impact of each unanswered question by summing the average potential benefits of all completeness graphs where that question appears. The question with the highest cumulative impact is identified as the high-priority question, ensuring that answering it first maximizes the likelihood of application approval or benefit maximization. This approach optimizes the application process by focusing on questions that have the greatest influence on the outcome, reducing delays and improving efficiency for both applicants and benefit agencies.
6. The method of claim 1 , further comprising receiving data through a user interface, wherein the profile comprises the received data.
A system and method for generating and managing user profiles involves collecting and processing data to create personalized profiles. The method includes gathering data from various sources, such as user inputs, device interactions, or external databases, to build a comprehensive profile. This profile is then used to customize user experiences, improve recommendations, or enhance system functionality. The method further involves receiving additional data through a user interface, which is incorporated into the profile to refine or update it. The system may analyze the data to identify patterns, preferences, or behaviors, enabling dynamic adjustments to the profile. The method ensures that the profile remains relevant and accurate by continuously integrating new data, allowing for adaptive and personalized interactions. The system may also include validation steps to verify the accuracy and reliability of the collected data before updating the profile. This approach enhances user engagement and system efficiency by leveraging real-time data inputs to maintain an up-to-date and detailed user profile.
7. The method of claim 1 , further comprising accessing previously collected data, wherein the profile comprises the accessed data.
A system and method for generating user profiles by incorporating previously collected data. The technology operates in the domain of data analysis and user profiling, addressing the challenge of creating comprehensive and accurate user profiles by leveraging historical data. The method involves collecting and analyzing user data from various sources, such as online interactions, transactions, or behavioral patterns, to build a detailed profile. This profile is then enhanced by accessing and integrating previously collected data, which may include demographic information, past preferences, or historical behavior. By combining real-time data with historical records, the system ensures that the user profile is both current and contextually rich. The integration of past data allows for more accurate predictions, personalized recommendations, and improved decision-making processes. This approach is particularly useful in applications like marketing, customer service, and personalized content delivery, where understanding user behavior and preferences is critical. The method ensures that the profile remains dynamic and continuously updated, adapting to changes in user behavior over time. The system may also include mechanisms to verify the accuracy and relevance of the accessed data, ensuring that the profile remains reliable and actionable.
8. The method of claim 1 , further comprising: receiving an answer to the high priority unanswered question; modifying the profile corresponding to the individual based on the received answer; modifying the respective sets of unanswered questions in each completeness graph in the set of completeness graphs by running the modified profile against each completeness graph; forming a modified collection of unanswered questions in the set of completeness graphs from the modified respective sets of unanswered questions in each completeness graph; and identifying, based on the modified profile, a new high priority unanswered question in the modified collection of unanswered questions using a prioritization algorithm.
This invention relates to adaptive question prioritization systems, particularly for dynamically updating user profiles and refining question selection based on received answers. The system addresses the challenge of efficiently gathering information from individuals by prioritizing questions in a way that maximizes data completeness and relevance. The method involves maintaining a set of completeness graphs, each representing a structured collection of questions and their relationships. Initially, a high-priority unanswered question is identified from these graphs using a prioritization algorithm, which considers factors like question importance and dependencies. Once an answer is received, the user's profile is updated, and the completeness graphs are re-evaluated by running the modified profile against each graph. This process generates updated sets of unanswered questions, which are then combined into a modified collection. The prioritization algorithm is reapplied to this collection to identify a new high-priority question, ensuring continuous refinement of the questioning process. The system dynamically adapts to new information, improving the efficiency and accuracy of data collection.
9. The method of claim 8 , wherein the new high priority unanswered question is identified in real-time when the answer is received.
This invention relates to real-time question prioritization in communication systems, particularly for handling unanswered questions in interactive environments such as customer support, chatbots, or collaborative platforms. The problem addressed is the inefficiency in managing high-priority unanswered questions, which can lead to delays, user frustration, or missed opportunities in time-sensitive interactions. The method involves dynamically identifying and prioritizing new high-priority unanswered questions in real-time as answers are received. When a response to a previously asked question is provided, the system evaluates the context to determine if a new, higher-priority question has emerged that requires immediate attention. This ensures that critical inquiries are addressed promptly, improving responsiveness and user satisfaction. The prioritization may be based on factors such as urgency, relevance, or predefined rules, and the system may automatically route or flag these questions for faster resolution. This approach enhances efficiency in environments where timely responses are crucial, such as customer service, technical support, or real-time collaboration tools.
10. The method of claim 1 , further comprising obtaining an answer to the high priority unanswered question.
A system and method for prioritizing and answering questions in a conversational interface, particularly in applications such as customer support or virtual assistants, addresses the challenge of efficiently managing high-priority unanswered questions. The method involves analyzing incoming questions to determine their priority based on factors such as urgency, relevance, or user importance. High-priority questions that remain unanswered are flagged for immediate attention. The system then obtains an answer to these high-priority questions, either by routing them to a human agent, retrieving a pre-existing solution from a knowledge base, or generating a response using an automated system. The method ensures that critical inquiries are addressed promptly, improving user satisfaction and operational efficiency. The system may also track response times and accuracy to refine prioritization algorithms over time. This approach is particularly useful in environments where timely responses are crucial, such as emergency services, healthcare, or high-volume customer support centers. The method integrates with existing conversational interfaces to enhance their effectiveness without requiring significant architectural changes.
11. The method of claim 1 , further comprising: generating a user interface; and presenting the high priority unanswered question through the user interface.
This invention relates to a system for managing and prioritizing unanswered questions in a digital environment, such as a customer support or knowledge management platform. The problem addressed is the inefficiency in identifying and addressing high-priority unanswered questions, which can lead to delays in resolving critical issues or providing important information. The method involves analyzing a set of unanswered questions to determine their priority levels. This analysis may consider factors such as the urgency of the question, the number of users affected, or the potential impact of the issue. Once the priority levels are determined, the system generates a user interface to display the highest-priority unanswered questions. This interface allows users, such as support agents or administrators, to quickly identify and address the most critical questions first, improving response times and overall efficiency. The system may also include additional features, such as categorizing questions based on their content or source, tracking the status of questions over time, and providing tools for collaboration or delegation of tasks. The goal is to streamline the process of managing unanswered questions, ensuring that the most important issues are resolved promptly while reducing the workload on support teams.
12. The method of claim 1 , wherein the high priority unanswered question is in a plurality of sets of unanswered questions.
A system and method for managing unanswered questions in a communication or support environment, particularly in automated or interactive systems such as chatbots, customer service platforms, or knowledge management tools. The problem addressed is the inefficient handling of unanswered questions, which can lead to user frustration, missed opportunities, or unresolved issues. The invention categorizes unanswered questions into multiple sets based on priority, ensuring that high-priority questions receive immediate attention while lower-priority questions are managed accordingly. This prioritization helps streamline workflows, improve response times for critical inquiries, and enhance overall system efficiency. The method may involve automated detection, classification, and routing of questions, possibly using machine learning or rule-based systems to determine priority levels. The invention may also integrate with existing support systems, databases, or user interfaces to provide a seamless experience. By organizing unanswered questions into distinct sets, the system ensures that important queries are addressed promptly, reducing backlogs and improving user satisfaction. The approach is particularly useful in environments where a large volume of questions is received, such as customer service centers, technical support platforms, or online forums. The invention may also include features for tracking, reporting, and escalating unanswered questions to ensure accountability and resolution.
13. The method of claim 1 , further comprising: evaluating missing data fields identified by the completeness graphs; and presenting suggested questions to be asked to the individual to fill in the missing data fields to be stored in the shared data store.
This invention relates to data collection systems that use completeness graphs to identify missing information in a shared data store. The system generates a completeness graph representing the availability of data fields for individuals, where nodes correspond to data fields and edges indicate relationships between them. The graph highlights missing data fields by analyzing the relationships and dependencies between fields. The system then evaluates these missing fields and presents suggested questions to individuals to fill in the gaps, ensuring the shared data store is complete. The method improves data accuracy and efficiency by dynamically identifying and addressing missing information based on the graph structure. The completeness graph may be updated in real-time as new data is collected, ensuring continuous improvement in data completeness. The system is particularly useful in applications requiring comprehensive data sets, such as surveys, medical records, or customer databases, where missing information can lead to incomplete analysis or decision-making. The invention automates the process of identifying and resolving data gaps, reducing manual effort and improving data quality.
14. The method of claim 1 , wherein a text explanation of each node is stored within respective memory locations in the shared data store.
A system and method for organizing and retrieving information in a hierarchical structure, such as a knowledge graph or decision tree, where nodes represent concepts, decisions, or data points. The invention addresses the challenge of efficiently storing and accessing detailed explanations for each node in a shared data store, ensuring clarity and context for users interacting with the structure. Each node in the hierarchy contains a text explanation that describes its purpose, relationships, or associated data. These explanations are stored in dedicated memory locations within the shared data store, allowing for quick retrieval and display when a user accesses a particular node. The shared data store may be a centralized database, distributed storage system, or cloud-based repository, enabling multiple users or applications to access the same hierarchical structure and its explanations simultaneously. The system ensures consistency and synchronization of the stored explanations, preventing conflicts or outdated information. This approach enhances usability by providing immediate context for each node, improving decision-making, knowledge sharing, and data analysis in applications such as artificial intelligence, business intelligence, or educational tools.
15. The method of claim 1 , wherein each node contains a question being logically expressed by the completeness graph that is answered in the affirmative or negative to minimize subsequent questions based on answers given to prior questions.
A method for optimizing decision-making processes using a completeness graph involves a system where each node represents a question that is logically structured to minimize subsequent inquiries based on prior answers. The completeness graph is a decision tree-like structure where each question is designed to narrow down possible outcomes efficiently. When a user answers a question in the affirmative or negative, the system uses that response to determine the next most relevant question, reducing redundancy and improving decision accuracy. This approach ensures that only necessary questions are asked, streamlining the decision-making process. The method is particularly useful in applications requiring rapid, accurate assessments, such as diagnostic systems, troubleshooting tools, or interactive decision support systems. By dynamically adjusting the sequence of questions based on prior responses, the system adapts to the user's input, enhancing efficiency and reducing cognitive load. The completeness graph's structure ensures that all critical decision points are covered while minimizing unnecessary steps, making it a robust solution for complex decision-making scenarios.
16. The method of claim 1 , wherein each node of respective completeness graph is associated to at least one pre-stored narratives stored within respective memory locations in the shared data store and the at least one pre-stored narrative is mapped to the node of respective completeness graph.
This invention relates to a system for managing and retrieving narratives in a shared data store, particularly for applications involving completeness graphs. The technology addresses the challenge of efficiently organizing and accessing pre-stored narratives linked to nodes within a completeness graph structure, ensuring that relevant narratives are quickly retrievable for analysis or decision-making processes. The system involves a shared data store that contains pre-stored narratives, each associated with specific nodes in a completeness graph. Each node in the graph is mapped to at least one narrative stored in the data store, with the narratives being stored in designated memory locations. This mapping allows for the retrieval of narratives based on their association with nodes, enabling users to access relevant information without manual searching. The completeness graph structure helps visualize relationships between different elements, while the linked narratives provide detailed context or supporting information for each node. The method ensures that narratives are pre-stored and readily available, reducing latency in retrieval and improving efficiency in applications such as knowledge management, decision support, or data analysis. By associating narratives with nodes in a completeness graph, the system enhances the usability of the data store, making it easier to navigate and extract meaningful insights. The invention is particularly useful in environments where quick access to contextual information is critical, such as legal, medical, or business analytics.
17. A computer-implemented method to execute a benefit analysis system for acquiring benefits qualification data, the benefit analysis system being stored at least partially in a memory of a computing system, the memory storing computer-executable instructions in conjunction with one or more processors of the computing system, the method being performed by the computing system and comprising: storing, in a shared data store of the benefit analysis system, benefit qualification data corresponding to an individual, the benefit qualification data comprising user input data and data imported from one or more online resources through at least one network; obtaining a profile including the benefit qualification data corresponding to the individual; for each of a plurality of benefit programs corresponding to a plurality of completeness graphs, obtaining at least one completeness graph, each completeness graph representing a completeness graph data structure configured to determine whether the profile satisfies the completeness graph and thereby qualifies for the respective benefit program different from another benefit program, wherein each completeness graph comprises a plurality of interconnecting functional nodes and a plurality of respective arcs, the plurality of interconnecting functional nodes and the plurality of respective arcs representing the rules and regulations for qualifying for the respective benefit program, the plurality of interconnecting functional nodes comprising a beginning node, a termination node, a plurality of intermediate nodes, at least one arc being between the beginning node and at least one intermediate node, at least one arc being between the termination node and at least one intermediate node, and at least one arc being between different intermediate nodes, wherein respective beginning and intermediate nodes correspond to a plurality of questions indicative of respective benefit qualification questions, and respective arcs correspond to respective answers to the respective benefit qualification questions; forming a schema, the schema comprising the profile and a set of completeness graphs, wherein each completeness graph in the set of the completeness graphs corresponds to a respective benefit program; generating a set of unanswered questions from each of the plurality of the completeness graphs by running the profile against each completeness graph to fill the respective beginning node and intermediate nodes of each completeness graph with data of the profile, each set of unanswered questions being different from another set of unanswered questions from another completeness graph; generating a collection of unanswered questions from a plurality of sets of the unanswered questions from the plurality of the completeness graphs in a manner that eliminates duplicated unanswered questions; and based on an average potential benefit associated with each completeness graph and obtained from a benefit agency database, identifying a high priority unanswered question in the collection of the unanswered questions from the plurality of the completeness graphs by executing a prioritization algorithm.
This invention relates to a computer-implemented benefit analysis system designed to streamline the process of determining an individual's eligibility for various benefit programs. The system addresses the challenge of efficiently gathering and evaluating the necessary qualification data across multiple benefit programs, which often have distinct and complex eligibility criteria. The system stores benefit qualification data for an individual in a shared data store, combining user-provided input with data imported from online resources via a network. It then constructs a profile for the individual using this data. For each benefit program, the system retrieves a corresponding completeness graph—a data structure representing the rules and regulations for qualifying for that program. Each completeness graph consists of interconnected functional nodes (beginning, intermediate, and termination nodes) and arcs that represent questions and answers related to benefit qualification. The system forms a schema by combining the individual's profile with a set of these completeness graphs, each tied to a different benefit program. By running the profile against each completeness graph, the system identifies unanswered questions specific to each program. These questions are then consolidated into a collection, removing duplicates. The system prioritizes the unanswered questions based on the average potential benefit associated with each program, using a prioritization algorithm to identify high-priority questions. This approach ensures that the most impactful questions are addressed first, optimizing the benefit qualification process.
18. The method of claim 17 , wherein the processing further comprises removing a completeness graph not satisfied by the profile from the set of completeness graphs by running the profile against the completeness graph before forming respective sets of unanswered questions.
A system and method for evaluating and refining data profiles using completeness graphs. The technology addresses the challenge of assessing whether a given data profile contains sufficient information to meet predefined completeness criteria, which is critical in applications like data validation, compliance, and decision-making. The method involves generating a set of completeness graphs, where each graph represents a structured set of questions or criteria that must be satisfied for a profile to be considered complete. The system processes a data profile by comparing it against each completeness graph to determine which questions or criteria remain unanswered. If a completeness graph is not satisfied by the profile, it is removed from the set, ensuring only relevant or achievable completeness criteria are considered. This refinement step helps focus subsequent analysis on the most pertinent completeness requirements, improving efficiency and accuracy in profile evaluation. The method may also involve generating sets of unanswered questions for each completeness graph that remains after the filtering step, providing a clear indication of missing information. This approach is particularly useful in scenarios where multiple completeness standards or criteria must be evaluated against a single data profile, such as regulatory compliance or data quality assurance.
19. A computing system to implement a benefit analysis system for acquiring benefits qualification data, the benefit analysis system comprising an input output module and a benefits calculation engine and being stored at least partially in a memory of a computing system, the memory storing computer-executable instructions being executed to cause the computing system to preform processing comprising: storing, in a shared data store of the benefit analysis system, benefit qualification data corresponding to an individual, the benefit qualification data comprising user input data and data imported from one or more online resources through at least one network; obtaining, by executing an input output module, a profile including the benefit qualification data corresponding to the individual; for each of a plurality of benefit programs corresponding to a plurality of completeness graphs, obtaining at least one completeness graph, each completeness graph representing a completeness graph data structure configured to determine whether the profile satisfies the completeness graph and thereby qualifies for the respective benefit program different from another benefit program, wherein each completeness graph comprises a plurality of interconnecting functional nodes and a plurality of respective arcs, the plurality of interconnecting functional nodes and the plurality of respective arcs representing the rules and regulations for qualifying for the respective benefit program, the plurality of interconnecting functional nodes comprising a beginning node, a termination node, a plurality of intermediate nodes, at least one arc being between the beginning node and at least one intermediate node, at least one arc being between the termination node and at least one intermediate node, and at least one arc being between different intermediate nodes, wherein respective beginning and intermediate nodes correspond to a plurality of questions indicative of respective benefit qualification questions, and respective arcs correspond to respective answers to the respective benefit qualification questions; by executing the benefits calculation engine, generating a set of unanswered questions from each of the plurality of the completeness graphs by running the profile against each completeness graph to fill the respective beginning node and intermediate nodes of each completeness graph with data of the profile, each set of unanswered questions being different from another set of unanswered questions from another completeness graph; generating a collection of unanswered questions from a plurality of sets of the unanswered questions from the plurality of the completeness graphs in a manner that eliminates duplicated unanswered questions; and based on an average potential benefit associated with each completeness graph and obtained from a benefit agency database, identifying a high priority unanswered question in the collection of the unanswered questions from the plurality of the completeness graphs by executing a prioritization algorithm.
The computing system implements a benefit analysis system to streamline the process of determining eligibility for various benefit programs. The system addresses the challenge of managing complex qualification rules across multiple programs by automating data collection and analysis. It stores benefit qualification data for an individual, including user-provided input and data imported from online resources. The system obtains a profile containing this data and evaluates it against multiple benefit programs, each represented by a completeness graph. These graphs are data structures that model the rules and regulations for qualifying for each program, consisting of interconnected nodes and arcs. Nodes represent questions related to benefit qualification, while arcs represent possible answers. The system processes the profile against each graph to identify unanswered questions, then consolidates these into a collection while eliminating duplicates. To prioritize the most impactful questions, the system uses a prioritization algorithm that considers the average potential benefit associated with each program, as obtained from a benefit agency database. This approach ensures that users are guided to provide the most relevant information first, improving efficiency in the qualification process.
20. The system of claim 19 , further comprising a memory configured to store the profile and the set of completeness graphs.
A system for managing and analyzing data completeness includes a processor and a memory. The processor generates a profile representing a data structure, such as a database schema, and constructs a set of completeness graphs. Each completeness graph maps relationships between data elements within the profile, indicating dependencies and completeness requirements. The memory stores the profile and the set of completeness graphs, allowing for retrieval and further analysis. The system may also include a display for visualizing the completeness graphs, helping users identify gaps or inconsistencies in the data structure. The processor can analyze the completeness graphs to detect missing or incomplete data elements, ensuring data integrity and accuracy. The system may further include a user interface for modifying the profile or completeness graphs, enabling dynamic updates to the data structure. The memory stores both the profile and the completeness graphs, ensuring that the relationships and dependencies between data elements are preserved for future reference and analysis. This system is particularly useful in environments where data completeness is critical, such as in database management, data integration, and compliance monitoring.
21. The system of claim 20 , wherein the memory is configured to store the profile and the set of completeness graphs as a schema.
A system for managing data profiles and completeness graphs is disclosed. The system addresses the challenge of efficiently organizing and retrieving structured data representations, particularly in applications requiring traceability and validation of data completeness. The system includes a memory configured to store a profile and a set of completeness graphs as a schema. The profile defines the structure and relationships of data elements, while the completeness graphs represent the degree to which data meets predefined criteria. The memory ensures that the profile and graphs are stored in a unified schema, enabling consistent access and processing. The system may also include a processor that generates or updates the completeness graphs based on input data, ensuring real-time validation and tracking of data completeness. This approach improves data integrity and simplifies compliance with regulatory or operational requirements by providing a structured framework for assessing and documenting data completeness. The system is particularly useful in industries where data accuracy and traceability are critical, such as healthcare, finance, or manufacturing. By storing the profile and completeness graphs as a schema, the system ensures that data relationships and completeness metrics are preserved and easily retrievable, enhancing overall data management efficiency.
22. The system of claim 19 , wherein the processing further comprises: by executing the benefits calculation engine, removing a completeness graph not satisfied by the profile from the set of completeness graphs by running the profile against the completeness graph before forming respective sets of unanswered questions.
The system relates to a data processing framework for evaluating user profiles against predefined completeness criteria. The problem addressed is the need to efficiently assess whether a user's profile meets specific completeness requirements, which may be represented as interconnected graphs of questions or data points. The system processes user profiles by comparing them against a set of completeness graphs, each representing a different set of criteria that must be satisfied. The processing involves running the user profile against each completeness graph to determine whether the profile meets the criteria defined by the graph. If a completeness graph is not satisfied by the profile, it is removed from the set of completeness graphs before forming respective sets of unanswered questions. This ensures that only relevant completeness criteria are considered, improving efficiency and accuracy in profile evaluation. The system may also include a benefits calculation engine that further processes the results to determine eligibility for benefits or other outcomes based on the completeness of the profile. The overall approach optimizes the evaluation process by dynamically filtering out irrelevant criteria, reducing computational overhead and improving decision-making accuracy.
23. The system of claim 22 , wherein running the profile against the completeness graph indicates that a likelihood that the profile would satisfy the completeness graph is lower than a threshold value.
A system for evaluating data completeness in a database or information system. The system addresses the challenge of ensuring that data records meet predefined completeness criteria, which is critical for data integrity, analytics, and decision-making. The system includes a completeness graph that defines the required attributes or conditions a data profile must satisfy to be considered complete. The system runs a data profile against this completeness graph to assess whether the profile meets the completeness criteria. If the likelihood that the profile would satisfy the completeness graph is lower than a predefined threshold value, the system identifies the profile as incomplete. This allows for automated detection of incomplete data, enabling corrective actions such as data enrichment, validation, or flagging for review. The system may also include mechanisms to generate alerts or reports based on the completeness assessment, ensuring that data quality issues are addressed proactively. The completeness graph can be dynamically updated to reflect evolving data requirements, making the system adaptable to changing business or regulatory needs. The system is particularly useful in environments where data accuracy and completeness are critical, such as financial systems, healthcare records, or enterprise databases.
24. The system of claim 19 , wherein the processing further comprises: by executing the benefits calculation engine, removing a duplicate unanswered question from the collection of unanswered questions.
The system is designed for managing and processing unanswered questions in a data-driven environment, such as customer support, surveys, or knowledge management systems. The problem addressed is the inefficiency caused by duplicate unanswered questions, which can lead to redundant processing, wasted resources, and delayed responses. The system includes a benefits calculation engine that analyzes a collection of unanswered questions to identify and remove duplicates, ensuring that only unique questions are processed further. This improves efficiency by reducing redundant work and optimizing resource allocation. The system may also include a question processing module that categorizes and prioritizes questions based on predefined criteria, such as urgency or relevance, to streamline the response process. Additionally, the system may integrate with external data sources to enrich question context, enabling more accurate duplicate detection and resolution. By automating the removal of duplicate questions, the system enhances overall productivity and ensures that responses are directed to unique inquiries, improving user satisfaction and operational efficiency.
25. The system of claim 19 , wherein the prioritization algorithm is stored in the benefits calculation engine and comprises: accessing a benefit agency database to obtain the average potential benefit corresponding to each completeness graph associated with the respective benefit program; determining a respective impact of each unanswered question in the collection of unanswered questions, the respective impact being equal to a sum of respective average potential benefits of each completeness graph in which the respective unanswered question appears; and identifying an unanswered question in the collection of unanswered questions with a highest impact as the high priority unanswered question.
The system relates to a benefits calculation engine that prioritizes unanswered questions in a benefits application process to maximize potential benefits for applicants. The problem addressed is the inefficiency in benefits applications where applicants may not know which questions to prioritize, leading to incomplete submissions and delayed or reduced benefits. The system includes a prioritization algorithm that dynamically identifies the most impactful unanswered questions based on their potential to increase the applicant's benefits. The algorithm accesses a benefit agency database to retrieve average potential benefits associated with each completeness graph, which represents different stages of application completion for various benefit programs. For each unanswered question, the system calculates its impact by summing the average potential benefits of all completeness graphs where the question appears. The question with the highest impact is flagged as a high-priority question, guiding the applicant to focus on the most beneficial questions first. This approach optimizes the application process by ensuring that the most valuable information is provided early, increasing the likelihood of maximizing benefits. The system is particularly useful in complex benefit programs where multiple questions influence eligibility and benefit amounts.
26. The system of claim 19 , further comprising a user interface configured to receive data, wherein the profile comprises the received data.
A system for managing user profiles includes a processor and a memory storing instructions that, when executed, cause the processor to generate and update a user profile based on received data. The system collects and processes data from various sources, such as user inputs, device interactions, or external databases, to create a comprehensive profile. This profile is dynamically updated as new data is received, ensuring it remains current and relevant. The system may also analyze the profile data to derive insights, such as user preferences, behavior patterns, or personalized recommendations. A user interface is integrated into the system to allow users to input additional data, which is then incorporated into their profile. This interface may include forms, input fields, or interactive elements that enable users to provide explicit feedback or additional context. The system ensures data privacy and security by implementing access controls and encryption mechanisms. The overall goal is to provide a personalized and adaptive experience by leveraging the collected and updated profile data.
27. The system of claim 19 , wherein the input output module is configured to access previously collected data, wherein the profile comprises the accessed data.
A system for data processing and analysis includes an input-output module that retrieves previously collected data and incorporates it into a user profile. The system operates in the domain of data management and user profiling, addressing the challenge of efficiently organizing and utilizing historical data to enhance personalized experiences or decision-making processes. The input-output module interfaces with data storage to access relevant datasets, which are then integrated into a profile structure. This profile may be used for various applications, such as personalized recommendations, behavioral analysis, or system customization. The system ensures that the profile dynamically updates with new or previously collected data, maintaining relevance and accuracy. By leveraging historical data, the system improves the efficiency and effectiveness of data-driven operations, reducing the need for redundant data collection and enhancing the reliability of user-specific insights. The overall architecture supports scalable and adaptive data integration, making it suitable for applications in fields like marketing, healthcare, or user experience optimization.
28. The system of claim 19 , wherein the input output module is configured to receive an answer to the high priority unanswered question, and wherein the benefits calculation engine is configured to: modify the profile corresponding to the individual based on the received answer, modify the respective sets of unanswered questions in each completeness graph in the set of completeness graphs by running the modified profile against each completeness graph, form a modified collection of unanswered questions in the set of completeness graphs from the modified respective sets of unanswered questions in each completeness graph, and identify, based on the modified profile, a new high priority unanswered question in the modified collection of unanswered questions using a prioritization algorithm.
The system operates in the domain of adaptive data collection and prioritization, addressing the challenge of efficiently gathering and prioritizing information from individuals to maximize the value of collected data. The system dynamically adjusts data collection priorities based on user responses, ensuring that the most relevant and impactful questions are presented next. The system includes an input/output module that receives answers to high-priority unanswered questions. A benefits calculation engine processes these answers by updating the individual's profile and reassessing the remaining unanswered questions. The engine modifies each completeness graph in a set of completeness graphs by running the updated profile against them, generating a new collection of unanswered questions. The engine then identifies a new high-priority unanswered question from this modified collection using a prioritization algorithm. This iterative process ensures that the system continuously refines its data collection strategy, adapting to new information and maintaining focus on the most valuable questions. The system is designed to optimize the efficiency and effectiveness of data gathering by dynamically prioritizing questions based on real-time responses.
29. The system of claim 28 , wherein the benefits calculation engine is configured to identify the new high priority unanswered question in real-time when the answer is received.
This invention relates to a system for managing and prioritizing unanswered questions in a real-time environment, particularly in applications such as customer support, knowledge management, or interactive platforms. The system addresses the challenge of efficiently identifying and prioritizing high-priority unanswered questions to ensure timely responses and improve user satisfaction. The system includes a benefits calculation engine that dynamically evaluates incoming answers to questions. When an answer is received, the engine identifies a new high-priority unanswered question in real-time, ensuring that the most critical inquiries are addressed promptly. This prioritization is based on factors such as urgency, relevance, or user impact, allowing the system to adapt to changing demands without manual intervention. The system also includes a question prioritization module that ranks unanswered questions based on predefined criteria, such as time sensitivity, user importance, or system workload. The benefits calculation engine interacts with this module to continuously update the priority list, ensuring that the most urgent questions are highlighted for immediate attention. By automating the identification and prioritization of high-priority questions, the system enhances efficiency in environments where rapid response times are crucial, such as customer service, technical support, or interactive Q&A platforms. The real-time processing capability ensures that the system remains responsive to evolving priorities, reducing delays and improving overall user experience.
30. The system of claim 19 , wherein the input output module is configured to obtain an answer to the high priority unanswered question.
A system for managing and prioritizing questions in a communication or data processing environment addresses the challenge of efficiently handling high-priority unanswered questions. The system includes an input/output module that interfaces with users or data sources to collect and process questions. A priority assessment module evaluates the importance of each question, assigning higher priority to urgent or critical inquiries. A question tracking module monitors the status of questions, identifying those that remain unanswered. The system further includes a response generation module that provides answers to questions, either automatically or by routing them to appropriate personnel. The input/output module is specifically configured to obtain answers to high-priority unanswered questions, ensuring that critical inquiries are addressed promptly. This system improves efficiency in question resolution by prioritizing and tracking questions, reducing delays in obtaining answers to important queries. The integration of these modules allows for automated or semi-automated handling of questions, enhancing responsiveness in environments where timely answers are crucial, such as customer support, technical assistance, or data analysis.
31. The system of claim 19 , further comprising a user interface manager configured to generate a user interface and present the high priority unanswered question through the user interface.
This invention relates to a system for managing and prioritizing unanswered questions in a digital environment, such as a customer support or knowledge management platform. The problem addressed is the inefficiency in handling large volumes of unanswered questions, where important or urgent inquiries may be overlooked due to lack of prioritization. The system includes a question prioritization module that analyzes incoming questions to determine their priority based on factors such as urgency, relevance, or user importance. The system also includes a question routing module that assigns the highest-priority unanswered questions to appropriate responders, such as support agents or subject matter experts, to ensure timely resolution. Additionally, the system may include a user interface manager that generates a user interface to display the highest-priority unanswered questions, allowing responders to quickly identify and address critical inquiries. The system may also track response times and user feedback to refine prioritization algorithms over time. This ensures that the most important questions are addressed first, improving efficiency and user satisfaction in digital support or knowledge-sharing environments.
32. The system of claim 19 , wherein the high priority unanswered question is in a plurality of sets of unanswered questions.
This invention relates to a system for managing unanswered questions in a communication or information retrieval system. The problem addressed is the efficient prioritization and handling of unanswered questions, particularly in environments where multiple questions may be pending. The system organizes unanswered questions into sets, allowing for prioritization based on factors such as urgency, relevance, or user importance. A high-priority unanswered question is identified within these sets, ensuring that critical inquiries are addressed promptly. The system may also include mechanisms for tracking, categorizing, and routing questions to appropriate responders. The prioritization process may involve analyzing metadata associated with each question, such as timestamps, sender information, or content keywords, to determine its relative importance. The system may further integrate with other components, such as user interfaces, databases, or automated response systems, to facilitate the resolution of high-priority questions. The overall goal is to improve response efficiency and user satisfaction by systematically managing and prioritizing unanswered questions.
33. The system of claim 19 , wherein the processing further comprises: by executing the benefits calculation engine, evaluating missing data fields identified by the completeness graphs; and proposing suggested questions to be asked to the individual to fill in the missing data fields to be stored in the shared data store.
This invention relates to a data processing system designed to improve data completeness in shared data stores by identifying missing information and suggesting questions to fill gaps. The system operates within a framework that includes a benefits calculation engine and completeness graphs, which visually represent data completeness across various fields. The benefits calculation engine evaluates missing data fields highlighted by these graphs and generates suggested questions to prompt individuals for the missing information. These questions are tailored to elicit responses that will complete the data fields, ensuring a more comprehensive and accurate dataset. The system is particularly useful in applications where data integrity is critical, such as financial, healthcare, or customer relationship management systems, where incomplete data can lead to errors or inefficiencies. By automating the identification of missing data and proposing targeted questions, the system enhances data quality and reduces manual effort in data collection and validation. The completeness graphs provide a visual aid to track progress and prioritize data collection efforts, while the benefits calculation engine ensures that the suggested questions are relevant and actionable. This approach streamlines data management processes and improves decision-making based on complete and reliable datasets.
34. The system of claim 19 , wherein a text explanation of each node is stored within respective memory locations in the shared data store.
A system for managing and processing data nodes includes a shared data store that stores a plurality of nodes, where each node represents a data element or a relationship between data elements. The system further includes a processing module that performs operations on the nodes, such as creating, modifying, or deleting them, and a user interface that allows users to interact with the nodes. The system is designed to facilitate efficient data organization, retrieval, and manipulation by maintaining a structured representation of data and relationships. In this specific implementation, the system stores a text explanation for each node within respective memory locations in the shared data store. These explanations provide additional context or metadata about the nodes, enhancing the system's usability by allowing users to quickly understand the purpose or content of each node without needing to access the underlying data directly. The text explanations may include descriptions, annotations, or other relevant information that aids in data interpretation and decision-making. This feature improves the system's ability to support collaborative work, documentation, and knowledge sharing by making node-specific information readily accessible. The system ensures that the text explanations are stored in a structured manner, enabling efficient retrieval and association with their corresponding nodes.
35. The system of claim 19 , wherein each node contains a question being logically expressed by the completeness graph that is answered in the affirmative or negative to minimize subsequent questions based on answers given to prior questions.
A system for optimizing decision-making processes using a completeness graph structure. The system addresses the problem of inefficient questioning or decision paths by dynamically adapting subsequent queries based on prior responses. The core system includes a network of interconnected nodes, each representing a decision point or question. The completeness graph ensures that all necessary questions are posed in an optimal sequence to reach a conclusion with minimal redundancy. Each node in the system contains a specific question that is logically structured to be answered either affirmatively or negatively. The system uses these answers to determine the most relevant follow-up questions, thereby minimizing the number of questions required to reach a final decision. This adaptive questioning approach improves efficiency by avoiding irrelevant or redundant inquiries, particularly in complex decision-making scenarios where multiple factors must be considered. The system is designed to be scalable and adaptable, allowing integration into various applications requiring structured decision-making, such as diagnostic tools, troubleshooting systems, or interactive decision support platforms. The dynamic nature of the completeness graph ensures that the system remains efficient even as new questions or decision criteria are introduced.
36. The system of claim 19 , wherein each node of respective completeness graph is associated to at least one pre-stored narratives stored within respective memory locations in the shared data store and the at least one pre-stored narrative is mapped to the node of respective completeness graph.
This invention relates to a system for managing and analyzing completeness graphs, which are used to represent the structure and relationships of data elements in a shared data store. The system addresses the challenge of efficiently organizing and retrieving pre-stored narratives associated with nodes in these graphs, ensuring that the narratives are accurately mapped to their corresponding nodes for quick access and analysis. The system includes a shared data store that holds multiple completeness graphs, each representing a different dataset or domain. Each node within these graphs is linked to at least one pre-stored narrative, which is stored in a designated memory location within the shared data store. The narratives are mapped directly to their respective nodes, allowing for seamless retrieval and association. This mapping ensures that when a node is accessed, its corresponding narrative is readily available, improving data retrieval efficiency and maintaining context. The system also includes a processing unit that manages the storage, retrieval, and mapping of these narratives. The processing unit ensures that the narratives remain accurately associated with their nodes, even as the completeness graphs are updated or modified. This dynamic association allows for real-time updates and ensures that the system remains current with the latest data. By integrating pre-stored narratives directly into the completeness graphs, the system enhances data organization and retrieval, making it easier for users to access relevant information quickly. This approach is particularly useful in applications requiring structured data analysis, such as knowledge management, decision support, and data-driven research.
37. A computer program product comprising a non-transitory computer readable storage medium embodying instructions executable by a computing system to implement a benefit analysis system, the benefit analysis system having an input output module and a benefits calculation engine, being stored at least partially in a memory of the computing system, and functioning in conjunction with one or more processors of the computing system, the instructions being executed to perform a process for acquiring benefit qualification data, the process comprising: obtaining, by executing the input output module, a profile including the benefit qualification data corresponding to an individual; for each of a plurality of benefit programs corresponding to a plurality of completeness graphs, obtaining at least one completeness graph, each completeness graph representing a completeness graph data structure configured to determine whether the profile satisfies the completeness graph and thereby qualifies for the respective benefit program different from another benefit program, wherein each completeness graph comprises a plurality of interconnecting functional nodes and a plurality of respective arcs, the plurality of interconnecting functional nodes and the plurality of respective arcs representing the rules and regulations for qualifying for the respective benefit program, the plurality of interconnecting functional nodes comprising a beginning node, a termination node, a plurality of intermediate nodes, at least one arc being between the beginning node and at least one intermediate node, at least one arc being between the termination node and at least one intermediate node, and at least one arc being between different intermediate nodes, wherein respective beginning and intermediate nodes correspond to a plurality of questions indicative of respective benefit qualification questions, and respective arcs correspond to respective answers to the respective benefit qualification questions; by executing the benefits calculation engine, generating a set of unanswered questions from each of the plurality of the completeness graphs by running the profile against each completeness graph to fill the respective beginning node and intermediate nodes of each completeness graph with data of the profile, each set of unanswered questions being different from another set of unanswered questions from another completeness graph; generating a collection of unanswered questions from a plurality of sets of the unanswered questions from the plurality of the completeness graphs in a manner that eliminates duplicated unanswered questions; and based on an average potential benefit associated with each completeness graph and obtained from a benefit agency database, identifying a high priority unanswered question in the collection of the unanswered questions from the plurality of the completeness graphs by executing a prioritization algorithm.
This invention relates to a benefit analysis system that evaluates an individual's eligibility for multiple benefit programs using a graph-based approach. The system addresses the challenge of efficiently determining which benefit programs an individual qualifies for by analyzing their profile data against program-specific rules and regulations. The system includes an input/output module and a benefits calculation engine. The input/output module obtains a profile containing benefit qualification data for an individual. The benefits calculation engine processes this data against multiple completeness graphs, each representing a different benefit program. Each completeness graph is a data structure with interconnected functional nodes and arcs, where nodes correspond to qualification questions and arcs represent possible answers. The system runs the profile data through each graph to identify unanswered questions, then consolidates these questions into a collection, removing duplicates. The system prioritizes unanswered questions based on the average potential benefit associated with each program, using a prioritization algorithm to identify high-priority questions. This approach streamlines the benefit qualification process by focusing on the most impactful missing information.
38. A computer program product comprising a non-transitory computer readable storage medium embodying instructions to execute a benefit analysis system having an input output module and a benefits calculation engine, being stored at least partially in a memory of the computing system, and functioning in conjunction with one or more processors of the computing system, the instructions being executed to cause the computing system to perform a process for acquiring benefit qualification data, the process comprising: storing, in a shared data store of the benefit analysis system, benefit qualification data corresponding to an individual, the benefit qualification data comprising user input data and data imported from one or more online resources through at least one network; obtaining, by executing the input output module, a profile including the benefit qualification data corresponding to an individual; for each of a plurality of benefit programs corresponding to a plurality of completeness graphs, obtaining at least one completeness graph, each completeness graph representing a completeness graph data structure configured to determine whether the profile satisfies the completeness graph and thereby qualifies for the respective benefit program different from another benefit program, wherein each completeness graph comprises a plurality of interconnecting functional nodes and a plurality of respective arcs, the plurality of interconnecting functional nodes and the plurality of respective arcs representing the rules and regulations for qualifying for the respective benefit program, the plurality of interconnecting functional nodes comprising a beginning node, a termination node, a plurality of intermediate nodes, at least one arc being between the beginning node and at least one intermediate node, at least one arc being between the termination node and at least one intermediate node, and at least one arc being between different intermediate nodes, wherein respective beginning and intermediate nodes correspond to a plurality of questions indicative of respective benefit qualification questions, and respective arcs correspond to respective answers to the respective benefit qualification questions; by executing the benefits calculation engine, forming a schema by executing the benefits calculation engine, the schema comprising the profile and a set of completeness graphs, wherein each completeness graph in the set of completeness graphs corresponds to a respective benefit program, wherein each completeness graph corresponds to a respective benefit program; generating a set of unanswered questions from each of the plurality of the completeness graphs by running the profile against each completeness graph to fill the respective beginning node and intermediate nodes of each completeness graph with data of the profile, each set of unanswered questions being different from another set of unanswered questions from another completeness graph; generating a collection of unanswered questions from a plurality of sets of the unanswered questions from the plurality of the completeness graphs in a manner that eliminates duplicated unanswered questions; and based on an average potential benefit associated with each completeness graph and obtained from a benefit agency database, identifying a high priority unanswered question in the collection of the unanswered questions from the plurality of the completeness graphs by executing a prioritization algorithm.
This invention relates to a benefit analysis system that automates the process of determining an individual's eligibility for various benefit programs. The system addresses the challenge of navigating complex qualification rules across multiple programs, which often require extensive manual data collection and analysis. The system includes an input/output module and a benefits calculation engine that work together to gather and process benefit qualification data. The data is stored in a shared data store and includes user-provided information and data imported from online resources. The system obtains a profile for an individual, which contains the collected benefit qualification data. For each benefit program, the system retrieves a completeness graph—a data structure representing the rules and regulations for qualifying for that program. Each completeness graph consists of interconnected nodes and arcs, where nodes correspond to qualification questions and arcs represent possible answers. The system forms a schema by combining the individual's profile with the completeness graphs for all relevant programs. It then generates unanswered questions for each program, consolidates them into a collection while eliminating duplicates, and prioritizes the questions based on the average potential benefit associated with each program. This prioritization helps streamline the data collection process by focusing on the most impactful questions first. The system is designed to efficiently determine eligibility across multiple benefit programs while minimizing redundant inquiries.
Unknown
December 22, 2020
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